The <i>BABAR</i> Long Term Data Preservation and Computing Infrastructure
Bibliographic record
Abstract
B A B AR stopped data collection in 2008, but its data is still analyzed by the collaboration. In 2021, a new computing system outside the SLAC National Accelerator Laboratory was developed, the new B A B AR Long Term Data Analysis system (LTDA). Major changes were needed to maintain the collaboration’s ability to analyze the data, while the user-facing front ends needed to remain unchanged. This LTDA system was put into production in 2022, and we will describe its unique infrastructure, which is based on cloud computing resources in Victoria, Canada; data storage at GridKa, Germany, with streaming data access via XRootD; and the ability to analyze data from any location. We will describe the advantages of the system, explain how to run an old and outdated OS in current infrastructures, discuss complications encountered during system development, and share our experience running and using it for more than two years. The design and implementation can help other groups and experiments planing data preservation with the goal of maintaining the ability to analyze their data, even decades after data collection has ceased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".